Consensus-guided individual graph learning via enhanced tensor low-rank for robust multi-view clustering.

Area of Science:

• Machine Learning
• Data Mining
• Computer Vision

Background:

• Existing graph-based multi-view clustering methods often suffer from information loss and error accumulation.
• They oversimplify inter-view relationships, neglecting the synergy between diversity, consistency, and higher-order correlations.

Purpose of the Study:

• To propose a novel method, Consensus-guided Individual Graph Learning via Enhanced Tensor Low-Rank (CIGETL), to address limitations in current multi-view clustering techniques.
• To improve the learning of individual graphs by using a consensus graph for guidance, thereby enhancing consistency and shared information capture across views.

Main Methods:

• CIGETL learns consistent representations in a common subspace to construct an initial consensus graph.
• This consensus graph is then used to guide the reconstruction of individual graphs within each view, acting as a dictionary for self-representation.
• It incorporates Double Laplacian manifold constraints for balancing diversity and consistency, column sum constraints for adaptability, and enhanced tensor low-rank minimization for capturing higher-order correlations.

Main Results:

• Extensive experiments were conducted on six public datasets.
• CIGETL demonstrated superior clustering performance compared to existing multi-view clustering methods.
• The proposed method also showed enhanced robustness in clustering tasks.

Conclusions:

• CIGETL offers a significant advancement in multi-view clustering by effectively leveraging consensus information to guide individual graph learning.
• The method successfully addresses issues of information loss, error accumulation, and oversimplified inter-view relationships.
• CIGETL provides a robust and high-performing solution for complex multi-view data analysis.